OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Energy-efficient VM Consolidation for Cloud Based Data Center - Revisited
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, a re-evaluation undertaken for dynamic VM consolidation problem and optimal online deterministic algorithms for the single VM migration in an experimental environment. We proceeded to focus on energy and performance trade-off by planet lab workload traces, which consists of a thousand Planetlab VMs with widespread simulation environments. All experiments are done in a simulated cloud environment by the CloudSim simulation tool. A new paradigm of utility-oriented IT services is cloud computing, which offers a pay-as-you-go model. In recent years, there has been increasing interest among many users from business, scientific, engineering and educational territories in cloud computing. There is increasing concern that high energy consumption issues are a disadvantage for various institutions. However, so far too little attention has been given to the various methods to reduce energy consumption in cloud environments while ensuring performance. Besides the evaluation of energy-efficient data center management algorithms in the cloud, we proposed a further research directed toward the development of energy efficient algorithms. By the experimental evaluation of the current proposal for the competitive analysis of dynamic VM consolidation and optimal online deterministic algorithms for the single VM migration, we found different results for different algorithm combinations. Cloud-based data centers` consume massive energy, which has a negative effect on the environment and operational cost, this work contributes to the energy consumption reduction in the cloud environment.
A Large-Scale Study of Call Graph-based Impact Prediction using Mutation Testing
Venue: arXiv cs.SE 2018
Year: 2018

In software engineering, impact analysis involves predicting the software elements (e.g., modules, classes, methods) potentially impacted by a change in the source code. Impact analysis is required to optimize the testing effort. In this paper, we propose an evaluation technique to predict impact propagation. Based on 10 open-source Java projects and 5 classical mutation operators, we create 17,000 mutants and study how the error they introduce propagates. This evaluation technique enables us to analyze impact prediction based on four types of call graph. Our results show that graph sophistication increases the completeness of impact prediction. However, and surprisingly to us, the most basic call graph gives the best trade-off between precision and recall for impact prediction.
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Venue: arXiv cs.SY 2018
Year: 2018

Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives.
Optimal Regular Expressions for Permutations
Venue: arXiv cs.FL 2018
Year: 2018

The permutation language $P_n$ consists of all words that are permutations of a fixed alphabet of size $n$. Using divide-and-conquer, we construct a regular expression $R_n$ that specifies $P_n$. We then give explicit bounds for the length of $R_n$, which we find to be $4^n n^{-(\lg n)/4+Θ(1)}$, and use these bounds to show that $R_n$ has minimum size over all regular expressions specifying $P_n$.
Evaluating Row Buffer Locality in Future Non-Volatile Main Memories
Venue: arXiv cs.AR 2018
Year: 2018

DRAM-based main memories have read operations that destroy the read data, and as a result, must buffer large amounts of data on each array access to keep chip costs low. Unfortunately, system-level trends such as increased memory contention in multi-core architectures and data mapping schemes that improve memory parallelism may cause only a small amount of the buffered data to be accessed. This makes buffering large amounts of data on every memory array access energy-inefficient. Emerging non-volatile memories (NVMs) such as PCM, STT-RAM, and RRAM, however, do not have destructive read operations, opening up opportunities for employing small row buffers without incurring additional area penalty and/or design complexity. In this work, we discuss architectural changes to enable small row buffers at a low cost in NVMs. We provide a memory access protocol, energy model, and timing model to enable further system-level evaluation. We evaluate the system-level tradeoffs of employing different row buffer sizes in NVM main memories in terms of energy, performance, and endurance, with different data mapping schemes. We find that on a multi-core CMP system, reducing the row buffer size can greatly reduce main memory dynamic energy compared to a DRAM baseline with large row sizes, without greatly affecting endurance, and for some memories, leads to improved performance.
Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Facial expression recognition has been an active area in computer vision with application areas including animation, social robots, personalized banking, etc. In this study, we explore the problem of image classification for detecting facial expressions based on features extracted from pre-trained convolutional neural networks trained on ImageNet database. Features are extracted and transferred to a Linear Support Vector Machine for classification. All experiments are performed on two publicly available datasets such as JAFFE and CK+ database. The results show that representations learned from pre-trained networks for a task such as object recognition can be transferred, and used for facial expression recognition. Furthermore, for a small dataset, using features from earlier layers of the VGG19 network provides better classification accuracy. Accuracies of 92.26% and 92.86% were achieved for the CK+ and JAFFE datasets respectively.
Embedding Cardinality Constraints in Neural Link Predictors
Venue: arXiv cs.AI 2018
Year: 2018

Neural link predictors learn distributed representations of entities and relations in a knowledge graph. They are remarkably powerful in the link prediction and knowledge base completion tasks, mainly due to the learned representations that capture important statistical dependencies in the data. Recent works in the area have focused on either designing new scoring functions or incorporating extra information into the learning process to improve the representations. Yet the representations are mostly learned from the observed links between entities, ignoring commonsense or schema knowledge associated with the relations in the graph. A fundamental aspect of the topology of relational data is the cardinality information, which bounds the number of predictions given for a relation between a minimum and maximum frequency. In this paper, we propose a new regularisation approach to incorporate relation cardinality constraints to any existing neural link predictor without affecting their efficiency or scalability. Our regularisation term aims to impose boundaries on the number of predictions with high probability, thus, structuring the embeddings space to respect commonsense cardinality assumptions resulting in better representations. Experimental results on Freebase, WordNet and YAGO show that, given suitable prior knowledge, the proposed method positively impacts the predictive accuracy of downstream link prediction tasks.
Effective Capacity for Renewal Service Processes with Applications to HARQ Systems
Venue: arXiv cs.IT 2018
Year: 2018

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Based on Z-transform, we derive exact analytical expressions for the effective capacity at a given quality of service (QoS) exponent for both the renewal processes with constant reward and with variable rewards. Unlike prior literature that the effective capacity is approximated with no many insightful discussions, our expression is simple and reveals further meaningful results, such as the monotonicity and bounds of effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ) and cross-packet HARQ (XP-HARQ). Numerical results corroborate the analytical ones and prove the superiority of our proposed approach. Furthermore, targeting at maximizing the effective capacity via the optimal rate selection, it is revealed that VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ.
Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks
Venue: arXiv cs.MM 2018
Year: 2018

Recently, the growing demand for rich multimedia content such as Video on Demand (VoD) has made the data transmission from content delivery networks (CDN) to end-users quite challenging. Edge networks have been proposed as an extension to CDN networks to alleviate this excessive data transfer through caching and to delegate the computation tasks to edge servers. To maximize the caching efficiency in the edge networks, different Mobile Edge Computing (MEC) servers assist each others to effectively select which content to store and the appropriate computation tasks to process. In this paper, we adopt a collaborative caching and transcoding model for VoD in MEC networks. However, unlike other models in the literature, different chunks of the same video are not fetched and cached in the same MEC server. Instead, neighboring servers will collaborate to store and transcode different video chunks and consequently optimize the limited resources usage. Since we are dealing with chunks caching and processing, we propose to maximize the edge efficiency by studying the viewers watching pattern and designing a probabilistic model where chunks popularities are evaluated. Based on this model, popularity-aware policies, namely Proactive caching policy (PcP) and Cache replacement Policy (CrP), are introduced to cache only highest probably requested chunks. In addition to PcP and CrP, an online algorithm (PCCP) is proposed to schedule the collaborative caching and processing. The evaluation results prove that our model and policies give better performance than approaches using conventional replacement policies. This improvement reaches up to 50% in some cases.
Voiceprint recognition of Parkinson patients based on deep learning
Venue: arXiv cs.SD 2018
Year: 2018

More than 90% of the Parkinson Disease (PD) patients suffer from vocal disorders. Speech impairment is already indicator of PD. This study focuses on PD diagnosis through voiceprint features. In this paper, a method based on Deep Neural Network (DNN) recognition and classification combined with Mini-Batch Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy people using voiceprint features. In order to exact the voiceprint features from patients, Weighted Mel Frequency Cepstrum Coefficients (WMFCC) is applied. The proposed method is tested on experimental data obtained by the voice recordings of three sustained vowels /a/, /o/ and /u/ from participants (48 PD and 20 healthy people). The results show that the proposed method achieves a high accuracy of diagnosis of PD patients from healthy people, than the conventional methods like Support Vector Machine (SVM) and other mentioned in this paper. The accuracy achieved is 89.5%. WMFCC approach can solve the problem that the high-order cepstrum coefficients are small and the features component's representation ability to the audio is weak. MBGD reduces the computational loads of the loss function, and increases the training speed of the system. DNN classifier enhances the classification ability of voiceprint features. Therefore, the above approaches can provide a solid solution for the quick auxiliary diagnosis of PD in early stage.
Feature Fusion Effects of Tensor Product Representation on (De)Compositional Network for Caption Generation for Images
Venue: arXiv cs.CV 2018
Year: 2018

Progress in image captioning is gradually getting complex as researchers try to generalized the model and define the representation between visual features and natural language processing. This work tried to define such kind of relationship in the form of representation called Tensor Product Representation (TPR) which generalized the scheme of language modeling and structuring the linguistic attributes (related to grammar and parts of speech of language) which will provide a much better structure and grammatically correct sentence. TPR enables better and unique representation and structuring of the feature space and will enable better sentence composition from these representations. A large part of the different ways of defining and improving these TPR are discussed and their performance with respect to the traditional procedures and feature representations are evaluated for image captioning application. The new models achieved considerable improvement than the corresponding previous architectures.
Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients (ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate cancer (PCa) via semi-supervised adversarial learning. Specifically, our synthesizer generates mp-MRI data in a sequential manner: first generating ADC maps from 128-d latent vectors, followed by translating them to the T2w images. The synthesizer is trained in a semisupervised manner. In the supervised training process, a limited amount of paired ADC-T2w images and the corresponding ADC encodings are provided and the synthesizer learns the paired relationship by explicitly minimizing the reconstruction losses between synthetic and real images. To avoid overfitting limited ADC encodings, an unlimited amount of random latent vectors and unpaired ADC-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images. To improve the robustness of synthesizing, we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only. A StitchLayer is then employed to fuse sub-images together in an interlaced manner into a full-size image. To enforce the synthetic images to indeed contain distinguishable CS PCa lesions, we propose to also maximize an auxiliary distance of Jensen-Shannon divergence (JSD) between CS and nonCS images. Experimental results show that our method can effectively synthesize a large variety of mpMRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship. Compared to the state-of-the-art synthesis methods, our method achieves a significant improvement in terms of both visual and quantitative evaluation metrics.
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
Venue: arXiv cs.LG 2018
Year: 2018

We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore, highly predictive features should be used first during classification in order to determine the set of possible output labels. Our methodology focuses the problem of designing resilient classifiers into a problem of designing resilient feature extractors for these highly predictive features. We provide two theorems, which support our methodology. The Serial Composition Resilience and Parallel Composition Resilience theorems show that the output of adversarially resilient feature extractors can be combined to create an equally resilient classifier. Based on our theoretical results, we outline the design of an adversarially resilient classifier.
Robust Graph Learning from Noisy Data
Venue: arXiv cs.CV 2018
Year: 2018

Learning graphs from data automatically has shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust PCA, where the quality of the graph plays a critical role. The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA. Thus, it boosts the clustering, semi-supervised classification, and data recovery performance overall. Extensive experiments on image/document clustering, object recognition, image shadow removal, and video background subtraction reveal that our model outperforms the previous state-of-the-art methods.
A Generic Information and Consent Framework for the IoT
Venue: arXiv cs.CY 2018
Year: 2018

The Internet of Things (IoT) raises specific issues in terms of information and consent, which makes the implementation of the General Data Protection Regulation (GDPR) challenging in this context. In this report, we propose a generic framework for information and consent in the IoT which is protective both for data subjects and for data controllers. We present a high level description of the framework, illustrate its generality through several technical solutions and case studies, and sketch a prototype implementation.
Minuet: A method to solve Sudoku puzzles by hand
Venue: arXiv cs.DM 2018
Year: 2018

This paper presents a systematic method to solve difficult 9 x 9 Sudoku puzzles by hand. While computer algorithms exist to solve these puzzles, these algorithms are not good for human's to use because they involve too many steps and require too much memory. For humans, all one can find in the literature are individual tricks, which used together in ad hoc ways can be used to solve some puzzles--but not all. To the author's knowledge, a systematic procedure made up of well-defined steps that can be carried out by hand and solve all puzzles has not been devised. This paper proposes one such technique--the "minuet" method. It is based on a new system of markings and a new way of simplifying the puzzles that can be easily carried out by hand--or by computer. The author has solved hundreds of puzzles of the most difficult kind ("evil" in Sudoku's parlance) and never found one that could not be solved. The average time to solve one of these puzzles is slightly over 1 hour. It is conjectured that this method can solve all well-posed 9 x 9 puzzles. The method's distinguishing feature is a "minuet" strategy that is applied when the puzzle cannot be further simplified with basic tricks. The strategy consists in concurrently developing two potential solutions, sometimes alone and sometimes in concert as if they were dancing a minuet.
Convolutional herbal prescription building method from multi-scale facial features
Venue: arXiv cs.CV 2018
Year: 2018

In Traditional Chinese Medicine (TCM), facial features are important basis for diagnosis and treatment. A doctor of TCM can prescribe according to a patient's physical indicators such as face, tongue, voice, symptoms, pulse. Previous works analyze and generate prescription according to symptoms. However, research work to mine the association between facial features and prescriptions has not been found for the time being. In this work, we try to use deep learning methods to mine the relationship between the patient's face and herbal prescriptions (TCM prescriptions), and propose to construct convolutional neural networks that generate TCM prescriptions according to the patient's face image. It is a novel and challenging job. In order to mine features from different granularities of faces, we design a multi-scale convolutional neural network based on three-grained face, which mines the patient's face information from the organs, local regions, and the entire face. Our experiments show that convolutional neural networks can learn relevant information from face to prescribe, and the multi-scale convolutional neural networks based on three-grained face perform better.
Multi Instance Learning For Unbalanced Data
Venue: arXiv cs.LG 2018
Year: 2018

In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is sufficiently rich, the SI method is a useful learning algorithm. In particular, we show that larger data imbalance, a quality that is typically perceived as negative, in fact implies a better resilience of the algorithm to the statistical dependencies of the objects in bags. In addition, our results shed new light on some known issues with the SI method in the setting of linear classifiers, and we show that these issues are significantly less likely to occur in the setting of neural networks. We demonstrate our results on a synthetic dataset, and on the COCO dataset for the problem of patch classification with weak image level labels derived from captions.
Probabilistic Attribute Tree in Convolutional Neural Networks for Facial Expression Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree structure organized according to attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. We further proposed a semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated samples to make the best use of available data. Experimental results on five facial expression datasets have demonstrated that the proposed PAT-CNN outperforms the baseline models by explicitly modeling attributes. More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of CNNs.